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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Advanced BigQuery Performance and Functionality
• Introduction to Processing Streaming Data
• Serverless Data Processing with Cloud Dataflow
• Serverless Messaging Using Cloud Sub/Pub
• Custom Model building Using SQL in BigQuery ML
• Prebuilt ML Models APIs for Unsaturated Data
• Performing Spark on Cloud Dataproc
• Creating a Data Lake
• Introduction to Data Engineering
• Bigtable Streaming Features and High-Throughput BigQuery
• Big Data Analytics with Cloud Al Platform Notebook
• Production ML Pipelines and use of Kubeflow
• Building a Data Warehouse
• Cloud Dataflow Streaming Features
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Introduction to Building Batch Data Pipelines
• Custom Model building Utilizing Cloud AutoML

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

Google Professional-Data-Engineer Exam Syllabus Topics:

Reference: https://cloud.google.com/certification/data-engineer

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